AI-Driven Intelligent Ticket Classification for Enterprise IT Service Management
DOI:
https://doi.org/10.15680/IJCTECE.2023.0606024Keywords:
: Enterprise IT Service Management, ITIL, Ticket Classification, Supervised Learning, Semi-Supervised Learning, Self-Supervised Learning, Data Foundations for ClassificationAbstract
In a typical IT service desk, the volume of incoming service tickets can exceed several thousand per week. Handling these tickets relies on an underlying classification or routing process that maps each ticket to qualified expert resources for resolution. The classification can be based on a predefined taxonomy, which provides a multi-level hierarchy of various types of IT service requests and distinguishes between incidents, problems, changes, queries, and other service requests. A traditional approach for ticket classification is based on manually crafted rules. However, such a rules-based approach is not always feasible, and classifications can frequently need to account for very rare or less frequently seen tickets. Even a rules-based classifier cannot always guarantee the effective usage of resources.
Machine learning has increasingly been used for automating the classification operation and shifting the IT service desk to a model-driven setup, where the classical rules-based approach is enhanced by a learning-based operation that learns the mapping function. AI-driven intelligent classifiers can cater to frequently seen tickets and can learn either a one-vs-each approach for every type of service request or process the ticket in a multi-task learning setup.
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